AI Agents: Theory and Practice
Memory bugs in AI agents usually come from bad writes, not weak recall. Learn how to design write policies that decide what an agent should persist, when it should write, and when it should not.
Procedural memory is not about facts or chat history. It is the reusable method an agent follows to complete recurring tasks. This article explains why skills matter, how they work, and why they make agents behave more like software systems than chat systems.
Most long-running agent failures are context failures, not reasoning failures. This article explains how context compaction keeps the hot path small, preserves recoverability, and turns memory management into an operational policy.
Long context windows do not solve long-running agent state. This article explains why summarization is really state compression, how to design structured summaries, when to trigger compaction, and how to detect behavioral drift before bad memory causes wrong tool calls or broken constraint handling.
Retrieval is not memory. In production AI agents for developers, retrieval should stay a read path to external systems, while memory remains a write path for learned continuity like preferences, instructions, and successful procedures.
Short-term state is not a transcript. It is the working memory of the current task. This article explains how clean, structured thread state improves agent focus, reduces context pollution, and makes tool-using systems easier to debug and scale.
Short-term state is not a transcript. It is the working memory of the current task. This article explains how clean, structured thread state improves agent focus, reduces context pollution, and makes tool-using systems easier to debug and scale.
State keeps an agent moving through the current thread. Memory persists selective information for future runs. External knowledge stays in source systems and should be retrieved on demand. This distinction matters because AI agent memory is a systems design problem, not a prompt trick.
Prompting matters, but it is only one control layer. Reliable agents come from designing the loop, validating tool use, tracing decisions, and treating runtime behavior as the real unit of quality.
AI agents usually do not fail with a dramatic crash. They fail quietly through wrong tool calls, invalid arguments, retry storms, looping behavior, and weak recovery. This article explains where the agent loop breaks and how to design traces, guardrails, limits, checkpointing, idempotency, and evals that catch incidents before users do.